Typical recommendation systems generate recommendations for things such as movies, books, items to purchase, restaurants, etc., using a variety of statistical or machine learning techniques, or combinations of such techniques to filter possible choices. For example, recommendation systems often use collaborative filtering or content-based filtering mechanisms to construct models for use in making recommendations, and may use combinations of both types of filtering.
Collaborative filtering approaches generally construct user-based models from information derived from observed user behaviors. Examples of behaviors evaluated to construct such models include prior user choices or purchases, and may also include user ratings for those choices or purchases. The resulting models are then used to predict other things of potential interest to users.
Content-based filtering approaches generally construct models based on item characteristics to recommend similar items to users. For example, if a user expresses interest in mystery movies, a content-based recommendation system would recommend other mystery movies. Further, such recommendations may be ordered or ranked using scores or the like generated from ratings provided by other users (i.e., combined content-based and collaborative filtering). Such recommendations can be further narrowed by attempting to identify ratings from users that have similar interests to a user for which a recommendation is being provided.